Novel Evaluation Methods for Multi-Level/Combination HIV Prevention Interventions

说明(申请人提供)：对“综合艾滋病毒预防”(我们最好的循证艾滋病毒预防干预措施包)进行社区一级评估的呼声日益高涨，这使得需要开发新的方法，既对综合干预措施的这些组合进行综合评估，又估计综合干预措施的个别组成部分的相对贡献。然而，这样的综合方法并不存在。社区病毒载量(CVL)概念的最新发展，为在社区层面评估T2转化性研究(床边到社区)提供了一个重要机会。CVL是从监测数据中对HIV-1病毒载量的综合生物测量，作为治疗介导的HIV病毒学抑制和HIV传播风险的人群水平标记。我们建议开发新的研究评估方法，通过结合两个正在开发的创新：1)社区病毒载量的计算生物学建模，以及2)程序输入、干预组件的过程测量和观察到的CVL变化的多层次过程路径分析，来评估联合干预中干预组件的相对贡献。具体目标是：目标1-社区病毒载量的计算生物学建模：在目标1中，我们将基于现有的监测和临床数据开发和测试旧金山的CVL计算生物学模型。目的2-多层次过程路径分析方法的发展：在目标2中，我们将结合从目标1中的CVL建模获得的数据和在组合HIV预防干预中可以观察到的干预成分的假设过程措施，来开发评估响应于多水平/组合HIV预防干预的CVL变化的方法。我们将利用结构方程模型(SEM)和对模拟CVL数据集的路径分析来估计干预因素的相对贡献，并估计竞争分析方法的敏感性和特异性。目标3-经验数据测试：在目标3中，我们将使用来自旧金山社区级组合艾滋病毒预防干预的经验数据，对目标2中确定的最佳候选过程路径模型进行贝塔测试，并评估方法的性能。意义：这些迫切需要的组合艾滋病毒预防干预评估方法的拟议发展有望为社区干预评估提供科学依据，并将建立将创新的CVL测量与扫描电子显微镜过程路径分析相结合的可行性。公共卫生意义：最终，发展这些在社区一级评估多级/组合预防干预措施的方法将为公共卫生政策制定者提供一个重要的工具，以评估这些潜在的高影响干预措施在社区一级的有效性，并评估哪些组成部分值得实施资源分配。 公共卫生相关性：这项研究将开发新的方法来评估社区一级的“组合艾滋病毒预防”(我们最好的循证艾滋病毒预防干预方案)。这些方法将为公共卫生政策制定者提供一个重要工具，以评估这些潜在的高影响组合艾滋病毒预防干预措施在社区一级的有效性，评估哪些组成部分值得实施，并帮助分配预防资源。

DESCRIPTION (provided by applicant): The increasing call for community-level evaluation of "combination HIV prevention" (packages of our best evidence-based HIV prevention interventions) has brought to the forefront the need to develop novel methods of both evaluating these combinations of interventions in aggregate and estimating the relative contributions of individual components of the combined intervention. However, such comprehensive methods do not exist. The recent development of the concept of Community Viral Load (CVL), presents a significant opportunity to evaluate T2 translational research (bedside-to-community) at the community level. CVL is an aggregate biologic measure of HIV-1 viral load from surveillance data that serves as a population-level marker of treatment mediated HIV virologic suppression and HIV transmission risk. We propose to develop novel research evaluation methods to assess the relative contribution of intervention components within a combination intervention by bringing together two developing innovations: 1) computational biology modeling of community viral load, and 2) multi-level process pathway analysis of program inputs, process measures of intervention components, and observed changes in CVL. The specific aims are: Aim 1 - Computational Biology Modeling of Community Viral Load (CVL): In Aim 1 we will develop and test a computational biology model of CVL for San Francisco based on existing surveillance and clinic data. Aim 2 - Multi-Level Process Path Analysis Methods Development: In Aim 2 we will develop methods for the evaluation of change in CVL in response to multi-level/combination HIV prevention interventions by combining data obtained from CVL modeling in Aim 1 and hypothetical process measures of intervention components that could be observed in a combination HIV prevention intervention. We will utilize Structured Equation Modeling (SEM) and path analysis on simulated CVL data sets to estimate the relative contribution of intervention elements and estimate the sensitivity and specificity of competing analytic approaches. Aim 3 - Empirical Data Testing: In Aim 3 we will beta-test the best candidate process path model identified in Aim 2 using empirical data from a community-level combination HIV prevention intervention in San Francisco and estimate the performance of the methods. Significance: The proposed development of these urgently needed methods for evaluation of combination HIV prevention interventions has the promise of informing the science of community-level intervention assessment and will establish the feasibility of combining innovative CVL measurement with SEM process pathway analysis. Public Health Significance: Ultimately, the development of these methods to evaluate multi- level/combination prevention interventions at the community level will provide public health policy makers with an important tool to assess the community level effectiveness of these potentially high impact interventions and to assess which components merit implementation resource allocation. PUBLIC HEALTH RELEVANCE: This research will develop new methods to evaluate community-level "combination HIV prevention" (packages of our best evidence-based HIV prevention interventions). These methods will provide public health policy makers with an important tool to assess the community level effectiveness of these potentially high impact combination HIV prevention interventions and to assess which components merit implementation and to help allocate prevention resources.